Data life cycle: Planning
What is data management planning?
Data management planning consists of defining the strategy that you plan to use for managing data and documentation generated within the project. It is about thinking upfront what’s the best way to avoid problems or unexpected costs related to data management, and set the conditions for your research data to achieve the highest possible impact in science, even after the end of the project.
Solutions regarding the handling of the data generated within a project is usually formalised in a Data Management Plan (DMP). A DMP is a document describing several aspects of the data management process which occur before, during and after the end of a project. Common components of a DMP are:
- general information about the project;
- description of the datasets that will be used and generated;
- use of metadata, ontologies and the way data documentation will be provided;
- storage solutions, data security and preservation strategy during and after the project;
- sharing of the data;
- costs and resources needed for data management;
- ethical and legal issues, such as privacy, intellectual property and licences.
Why is data management planning important?
It is good research practice to take care of your research data and have a DMP. It will make your work more efficient, facilitate team work and use of services and tools. Moreover, a detailed DMP would help in making your research data more FAIR. Advantages of making a DMP:
- it is often a requirement of research organisations and funders;
- it helps to plan and budget necessary resources and equipment;
- it defines roles and responsibilities in data management among the project team;
- it helps to identify risks in data handling and apply solutions at early stage;
- it facilitates data sharing, reuse and preservation.
What should be considered for data management planning?
Several aspects should be taken into account when making a data management plan.
Research organisation and funders often require a DMP as part of the application for grants or later when the project is funded. Therefore, consider guidelines, policies and tools for data management planning required by your funder.
Data management should be planned in the early stages of a research project. Preferably, the DMP should be filled in before starting data collection. However, the DMP is a living document and should be updated as the research project progresses to match e.g. an update of the infrastructures, research softwares or a novel collaboration.
Consider standards or best practices required by facilities and infrastructures that you plan to use.
Due to the variety of aspects that need to be addressed in a DMP, it is better to find recommendations and obtain help from your institution support services, such as IT department, library, data managers or data stewards, legal or tech transfer team and data protection officer.
Explore best practices, guidelines, tools and resources for research data management described in this website.
How to measure compliance to data management regulations and standards. Data management plan
How to write a Data Management Plan (DMP). Data protection
How to make research data compliant to GDPR. Project data management coordination
How to coordinate and organise data management activities in collaborative or multi-parter projects. Machine actionability
How to make machine-actionable (meta)data.